Faculty Opinions recommendation of Salmonella trafficking is defined by continuous dynamic interactions with the endolysosomal system.

2007 ◽  
Author(s):  
Sergio Grinstein
Keyword(s):  
Dynamic Interactions ◽  
Continuous Dynamic

scholarly journals Salmonella Trafficking is Defined by Continuous Dynamic Interactions with the Endolysosomal System

Traffic ◽  
2006 ◽  
Vol 8 (3) ◽  
pp. 212-225 ◽  
Author(s):  
Dan Drecktrah ◽  
Leigh A. Knodler ◽  
Dale Howe ◽  
Olivia Steele-Mortimer
Keyword(s):  
Dynamic Interactions ◽  
Continuous Dynamic

Solving the mechanisms responsible for organelle behavior in vivo by same-cell correlative light and electron microscopy

1992 ◽  
Vol 50 (1) ◽  
pp. 14-15
Author(s):  
Conly L. Rieder
Keyword(s):  
Electron Microscopy ◽  
Quantitative Analysis ◽  
Light Microscopy ◽  
Living Cell ◽  
Light And Electron Microscopy ◽  
Time Behavior ◽  
Dynamic Interactions ◽  
Positive Identification ◽  
Cellular Components

The behavior of many cellular components, and their dynamic interactions, can be characterized in the living cell with considerable spatial and temporal resolution by video-enhanced light microscopy (video-LM). Indeed, under the appropriate conditions video-LM can be used to determine the real-time behavior of organelles ≤ 25-nm in diameter (e.g., individual microtubules—see). However, when pushed to its limit the structures and components observed within the cell by video-LM cannot be resolved nor necessarily even identified, only detected. Positive identification and a quantitative analysis often requires the corresponding electron microcopy (EM).

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

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